REU Site: AI and Science Summer Lab: advancing AI-enabled scientific discovery across the physical and biological sciences

Artificial Intelligence (AI) and Machine Learning (ML) have become crucial foundations for all phases of the scientific method, from hypothesis generation to publication. It is increasingly critical that all students, irrespective of science domain, be trained from an early stage in their career on AI and ML foundations and methods. Leveraging AI in science without strong foundations may lead to negative impacts such as amplifying errors, magnifying biases, and misleading conclusions.

This Research Experiences for Undergraduates (REU) site will contribute to the advancement of knowledge with research into new AI techniques and application of these techniques to complex scientific problems. The four science focus areas—AI+Materials, AI+Earth, AI+Biology, and AI+Astrophysics—each have the potential for significant societal impacts: more efficient methods for developing batteries that are critical for storage and transportation; physics-based models for weather and climate to predict extreme weather events and improve climate predictions; modeling biological phenomena to understand complex interactions, predict health outcomes, and enable personalized medicine; and improving the use of cosmic probes to better understand the evolution of the universe.

This project will host the AI+Science Summer Lab REU site at the University of Chicago. The site will expose 10 students per year to cutting-edge research at the intersection of AI and Science over an 8-week program. The AI+Science Summer Lab REU site will train the next generation of AI researchers and scientists, pairing students with mentors in both AI and science domains.

It will provide the critical foundational skills in AI as well as the interdisciplinary grounding to apply these advancements to science. The site will include a rigorous and engaging program with weekly activities that are designed to educate students on the research process, teach skills to use throughout the program and beyond, and expose students to other areas of research.

It will also build a strong cohort environment to help students overcome the many challenges faced during research, create excitement around research, spur collaborations, and build long-lasting relationships well beyond the end of the program.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.